
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "72"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 92 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 88 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.563769	-0.972792	2.268301	0.977291	1.238704	1.947175	0.819521	0.318216	0.7821	0.7812	0.2223	16.837922	12.578367
2459593	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.881556	-1.074452	0.197612	1.363432	2.744143	1.425678	1.555738	0.028972	0.7061	0.6985	0.2157	5.786181	4.839704
2459592	RF_ok	100.00%	0.00%	0.00%	0.00%	-	-	3.053934	3.870176	37.800496	42.478906	24.525249	30.082375	1.178956	3.076504	0.6571	0.6720	0.4009	nan	nan
2459591	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	347.523470	347.897447	inf	inf	62.231966	59.485135	704.097755	659.049816	nan	nan	nan	0.000000	0.000000
2459590	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	358.858622	359.370419	inf	inf	34.112490	37.326756	248.174555	201.884725	nan	nan	nan	0.000000	0.000000
2459589	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.577055	-0.959625	0.822259	0.654462	2.106861	-0.744915	-0.079012	-0.600515	0.6800	0.6720	0.2393	6.081254	5.343784
2459588	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.829948	-1.142100	0.903906	1.787231	1.553121	0.361250	0.503640	0.085505	0.7641	0.7455	0.1934	9.087237	6.988611
2459587	RF_ok	100.00%	-	-	-	100.00%	0.00%	4.418891	-0.903531	0.547288	10.103981	-23.333991	-23.858603	-4.675000	-4.393628	nan	nan	nan	13.468687	13.146795
2459586	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.557755	1.208232	3.937079	1.973264	6.551925	5.596816	4.234953	1.038826	0.6249	0.6338	0.3055	4.367037	4.033403
2459585	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	33.027611	36.443493	14.072234	16.974075	4.431174	8.860139	6.717415	4.250285	0.6032	0.6086	0.3232	0.000000	0.000000
2459584	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.779792	5.913241	0.254733	7.203666	3.815840	22.823942	45.244838	40.856068	0.5814	0.5919	0.3152	11.275677	8.846581
2459583	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.010724	-0.905249	0.171968	3.665391	0.649334	3.927099	1.494280	-0.601886	0.6219	0.6294	0.3277	5.270153	4.872765
2459582	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	295.580920	296.149957	inf	inf	11376.136807	11408.423339	6070.081639	6088.519564	nan	nan	nan	0.000000	0.000000
2459581	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.105214	-0.909598	0.657771	3.542434	0.992626	4.858358	2.090745	-0.370227	0.6219	0.6277	0.3262	3.630199	3.547460
2459580	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.905320	-0.800200	0.055951	3.801473	1.537873	5.160282	3.528323	-0.507648	0.6195	0.6288	0.3236	5.501549	4.755457
2459579	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.112079	-0.737623	0.494437	4.347254	1.855369	5.458171	0.902708	-1.064088	0.6157	0.6266	0.3260	4.677030	4.257854
2459578	RF_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.507998	4.526924	20.285175	25.797687	34.735129	33.263416	31.209884	19.268207	0.6870	0.7030	0.4142	nan	nan
2459577	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.062235	-0.908621	0.403510	3.246588	1.013332	4.311009	2.203869	-0.223427	0.6216	0.6299	0.3251	3.818856	3.419634
2459576	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.935942	-0.737001	0.561341	2.022139	1.354450	2.366855	1.255660	-1.065842	0.6353	0.6451	0.3257	5.454059	4.731363
2459575	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.994358	-1.040637	0.162750	2.346022	0.312804	3.098446	0.130089	1.488961	0.7171	0.7122	0.2874	5.157754	4.991903
2459574	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.795313	-0.700283	0.224840	1.980387	2.414775	2.285607	2.733354	-0.651795	0.6416	0.6517	0.3215	4.247595	4.006821
2459573	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.021709	-0.570748	0.330711	5.181762	1.968255	4.916149	1.061203	-0.886397	0.6320	0.6420	0.3257	4.092831	3.741079
2459572	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.011926	-0.841467	0.298015	2.199015	0.956016	2.646790	0.798979	-1.438051	0.6259	0.6364	0.3258	4.073855	3.893801
2459571	RF_ok	100.00%	0.00%	0.00%	0.00%	-	-	3.766379	4.170876	21.787446	27.116852	92.479853	78.934953	44.395377	28.500083	0.6301	0.6509	0.3623	nan	nan
2459570	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.673204	-1.607888	0.931262	-1.592354	0.806029	-1.089614	1.539892	-0.008371	0.6324	0.6480	0.3091	6.995969	6.177178
2459569	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.698963	-0.509437	0.428021	2.688154	0.654213	1.688452	0.564328	2.029374	0.7405	0.7375	0.2007	5.603465	4.687230
2459566	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.071437	-0.780041	0.507053	3.354081	1.180950	3.595536	5.555337	0.840909	0.6140	0.6290	0.3435	3.198258	2.971769
2459565	RF_ok	100.00%	-	-	-	100.00%	0.00%	160514.135251	108952.296098	4301873.053672	5036982.707330	nan	nan	-5.052525	-5.052525	nan	nan	nan	0.000000	0.000000
2459564	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.417403	2.700330	0.805079	3.014548	-0.557072	0.198068	0.717328	-1.374061	0.6354	0.6258	0.3245	4.376325	2.827763
2459563	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.465742	6.706668	19.722320	24.441187	9.982540	16.241008	-0.523541	-1.696637	0.6215	0.6189	0.3269	4.825451	4.782971
2459562	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.366408	1.613251	-0.046569	1.068261	-0.856105	0.026820	-0.854231	-1.353416	0.6305	0.6302	0.3199	3.463684	2.901017
2459561	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.250133	2.406860	0.507215	1.690471	-0.556174	0.530053	-1.674497	-2.277953	0.6363	0.6372	0.3229	4.552883	3.900749
2459560	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459559	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.190006	2.533076	0.612729	1.943567	-0.270145	1.292264	-0.851845	-1.466026	0.6328	0.6322	0.3250	3.116817	2.672703
2459558	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.433647	6.903722	10.746680	12.879324	12.024782	16.312664	0.496203	-1.409863	0.6317	0.6214	0.3275	4.776810	3.947494
2459557	RF_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7713	0.7603	0.3263	nan	nan
2459556	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.767542	7.360476	11.106304	13.267823	10.379599	14.634323	-1.213103	-3.124848	0.6382	0.6312	0.3226	5.576731	4.452522
2459553	RF_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6324	0.6257	0.3298	nan	nan
2459552	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.507580	8.129920	14.264696	18.276548	16.065500	22.442593	0.671661	0.413104	0.6457	0.6351	0.3313	0.000000	0.000000
2459551	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.067557	1.983431	0.293214	1.776783	-0.951980	1.228561	-0.912038	-1.949235	0.6312	0.6236	0.3229	3.504979	2.771473
2459550	RF_ok	-	38.71%	38.71%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6388	0.6426	0.3883	nan	nan
2459549	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.337291	6.499608	10.641858	13.421524	10.989912	15.971691	-0.408860	-2.534931	0.6348	0.6295	0.3304	3.066831	2.798487
2459542	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.221560	6.755971	21.124758	26.812485	0.196587	1.160599	0.771633	-1.584246	0.6956	0.6865	0.3031	4.980286	4.052544
2459541	RF_ok	100.00%	1.79%	1.79%	0.00%	100.00%	0.00%	3.713569	5.511166	13.145922	15.753397	8.180270	12.300890	4.760413	4.262997	0.6949	0.6772	0.2823	4.219073	3.658692
2459540	RF_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.519655	3.211922	1.899278	2.950884	-0.163728	1.645751	-0.399645	-0.703362	0.7003	0.6852	0.3028	3.296248	2.707883
2459536	RF_ok	100.00%	37.06%	37.06%	0.00%	100.00%	0.00%	4.712059	9.991192	17.337570	21.926413	16.650282	22.212567	-0.529562	-4.227218	0.4206	0.4251	0.2082	2.081493	1.795266
2459535	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459534	RF_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.382207	2.155727	0.523056	1.767305	-1.614467	0.509829	0.417973	-1.396730	0.0763	0.0944	0.0191	1.140750	1.135054
2459533	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.994583	7.453655	11.017050	13.731468	9.002033	14.529049	-1.676975	-2.668733	0.0774	0.0914	0.0181	1.219387	1.214557
2459532	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	4.728132	8.669256	14.450370	17.550178	11.797324	17.499750	-0.638145	-2.332200	0.0707	0.0880	0.0169	0.831959	0.831793
2459530	RF_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.023223	2.146811	0.380223	1.488121	-0.931351	1.134885	-0.046826	-1.005492	0.0785	0.0971	0.0206	1.166232	1.161385
2459527	RF_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.041406	8.688900	11.763829	14.587600	23.987019	24.847757	-0.703382	-0.775246	0.9129	0.9353	0.2780	0.000000	0.000000
2459523	RF_ok	100.00%	-	-	-	100.00%	0.00%	4.009737	8.470322	15.845903	20.599239	8.875285	20.414345	-4.336233	-5.857479	nan	nan	nan	4.407024	3.899275
2459522	RF_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.468062	7.124413	14.453585	17.428300	5.965421	16.507915	-1.350913	-2.907986	0.7095	0.6824	0.3208	5.660275	4.870564
2459513	RF_ok	100.00%	100.00%	100.00%	0.00%	-	-	4.636933	5.190805	29.435587	31.890852	14.370237	15.414888	8.168949	7.245901	0.0503	0.0316	0.0032	nan	nan
2459508	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.973326	9.093231	9.421405	11.343226	9.469584	10.591357	8.446735	12.662609	0.0549	0.0968	0.0094	0.000000	0.000000
2459505	RF_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	106.945618	97.460145	110.233458	115.870403	92.148344	98.899152	513.800341	631.162376	0.0110	0.0083	0.0061	1.082923	1.082056

Antenna Season Report Notebook¶

Antenna 72 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 72, Node 4:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 72: 2459616

Antenna 72: 2459615

Antenna 72: 2459614

Antenna 72: 2459613

Antenna 72: 2459612

Antenna 72: 2459611

Antenna 72: 2459610

Antenna 72: 2459609

Antenna 72: 2459608

Antenna 72: 2459607

Antenna 72: 2459605

Antenna 72: 2459604

Antenna 72: 2459603

Antenna 72: 2459602

Antenna 72: 2459598

Antenna 72: 2459597

Antenna 72: 2459596

Antenna 72: 2459595

Antenna 72: 2459594

Antenna 72: 2459593

Antenna 72: 2459592

Antenna 72: 2459591

Antenna 72: 2459590

Antenna 72: 2459589

Antenna 72: 2459588

Antenna 72: 2459587

Antenna 72: 2459586

Antenna 72: 2459585

Antenna 72: 2459584

Antenna 72: 2459583

Antenna 72: 2459582

Antenna 72: 2459581

Antenna 72: 2459580

Antenna 72: 2459579

Antenna 72: 2459578

Antenna 72: 2459577

Antenna 72: 2459576

Antenna 72: 2459575

Antenna 72: 2459574

Antenna 72: 2459573

Antenna 72: 2459572

Antenna 72: 2459571

Antenna 72: 2459570

Antenna 72: 2459569

Antenna 72: 2459566

Antenna 72: 2459565

Antenna 72: 2459564

Antenna 72: 2459563

Antenna 72: 2459562

Antenna 72: 2459561

Antenna 72: 2459560

Antenna 72: 2459559

Antenna 72: 2459558

Antenna 72: 2459556

Antenna 72: 2459552

Antenna 72: 2459551

Antenna 72: 2459549

Antenna 72: 2459542

Antenna 72: 2459541

Antenna 72: 2459540

Antenna 72: 2459536

Antenna 72: 2459535

Antenna 72: 2459534

Antenna 72: 2459533

Antenna 72: 2459532

Antenna 72: 2459530

Antenna 72: 2459527

Antenna 72: 2459524

Antenna 72: 2459523

Antenna 72: 2459522

Antenna 72: 2459513

Antenna 72: 2459508

Antenna 72: 2459506

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶